Automatic canonical digital image selection method and apparatus

ABSTRACT

Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

FIELD OF THE DISCLOSURE

The present disclosure relates to selecting one or more canonicaldigital images from a set of digital images that depict an entity, suchas a person.

BACKGROUND

There are a significant number of digital images available via acomputing device to a user. Currently, digital image searching retrievesan image based on metadata associated with image and a query. By way ofone example, a digital image search might retrieve a number of digitalimages having a metadata term that is the same or similar to a searchquery term. This approach returns a number of digital images that varyin quality and that may not depict the subject matter, e.g., a person,which is of interest to the user. The approach requires considerabletime and effort on the part of the user, since the approach requiresthat the user open each digital image returned as part of the searchresults until the user finds a digital image that is contains thecontent for which the user entered the query.

SUMMARY

The present disclosure provides novel systems and methods for automaticselection of high quality, canonical digital images from a large corpusof digital images, such as the corpus of digital images available on theweb. Embodiments of the present disclosure provide an automated,unsupervised approach for selecting a diverse set of high quality,canonical digital images, which is well suited for a large corpus ofdigital images. In contrast to a supervised approach which requireslabeled training data (e.g., tagging of hundreds of thousands of digitalimages), the unsupervised approach used with embodiments of the presentdisclosure does not use labeled training data, which makes it easilyscalable to a very large number of digital images, including the corpusof digital images available over an electronics communication networksuch as the Internet, the web, etc., and a large number of entities.Embodiments of the present disclosure use an unsupervised approach toidentify a set of high quality, canonical digital images for eachentity, e.g., each person, point of interest, object etc., using anynumber (e.g., a small digital image library to the corpus of digitalimages available via the web) of unlabeled digital images. The set ofcanonical digital images identified for an entity can be retrieved inresponse to a digital image request, which may include a query, fordigital images depicting the entity.

Presently, digital image retrieval relies on the metadata associatedwith each digital image to retrieve a set of search results. Themetadata associated with a digital image may be inaccurate or ambiguous,which can easily lead to retrieval of digital images that do not depictthe requested entity. In addition and even in a case that the metadatais accurate and/or unambiguous, a digital image may not be an optimal(e.g., definitive, canonical, most representative, low quality)depiction of the entity.

The automatic selection of canonical digital images from a large corpusof digital images described herein enables the efficiency of processingof any size corpus of digital images and automatic selection, from thecorpus of digital images, of a number of digital images that are mostrepresentative of an entity. This presents improvements in the speed ofretrieval and distribution of high quality, representative digitalimages.

According to some embodiments, the disclosed systems and methods firstsearches for digital images using a number of text-based searches,selects a set of candidate digital images based on relevance to thesearch query(s), analyzes pixel data of each candidate digital image togenerate a feature vector representation of each candidate digitalimage, a clustering approach is used to form clusters, or groups, ofcandidate digital images, smaller clusters can be eliminated fromfurther consideration, and a number of digital images is selected fromeach of a number of the clusters. In selecting a number of images from acluster, each image in the cluster is given a score representing aquality of the image's depiction of the entity. In generating a scorefor a candidate digital image, a number of aspects of the candidatedigital image are considered. Some examples of considerations that canbe used to measure quality include a determined proportion of thecandidate digital image depicting the entity, a determination whether ornot the candidate digital image is a natural image (versus a sketch,cartoon, etc.), a determination of the aesthetics of the candidatedigital image, such as that the candidate digital image is not offensivecontent. Within a given cluster, the score associated with eachcandidate digital image can be used to rank, or order, the candidatedigital images. A number of the highest scoring candidate digital imagescan then be selected from each of the larger clusters. The selectedcandidate digital images from a given cluster represent the canonicaldigital images from the cluster. By clustering the candidate digitalimages based on feature vector similarity and then selecting a number ofdigital images from multiple clusters, and groupings within a cluster, adiverse set of canonical digital images can be determined for a givenentity.

It will be recognized from the disclosure herein that embodiments of theinstant disclosure provide improvements to a number of technology areas,for example those related to systems and processes that handle orprocess content retrieval and delivery to users over the internet, suchas but not limited to, search engines, digital content sharing webservices, or other types of media retrieval platforms, recommendationplatforms, electronic social networking platforms and the like.

The disclosed systems and methods can effectuate increased efficiencyand accuracy in the ways that users can retrieve and access digitalimage content, thereby minimizing user effort, as the disclosed systemsand methods, inter alia, reduce the amount of required effort for a userto search for, and retrieve, digital image content depicting a givenentity. Users benefit from the fully automated selection of canonicaldigital images of an entity provided by the disclosed systems andmethods. For example, the disclosed automated selection of canonicaldigital images “sifts through” a large corpus of digital images andautomatically selects a number of canonical digital images of an entityfor the user, so that such canonical digital images can be speedilypresented to the user in response to a retrieval request. This avoidsthe users having to view low-quality digital images of an entity, whichreduces a user's frustration thereby increasing the user's satisfactionwith a digital content provider's system. In addition, a digital contentsystem is improved and made more efficient, given that the number ofdigital images that are retrieved and/or transmitted over an electroniccommunications network to a client device can be optimized.

In accordance with one or more embodiments, a method is disclosed whichincludes receiving, at a computing device, a request for a set ofcanonical digital images of an entity; generating, via the computingdevice, a number of digital image search result sets, the search resultset generation comprising querying a number of digital image data storesusing a number of queries, each query comprising a number of searchterms; selecting, via the computing device, a plurality of candidatedigital images from the number of digital image search result sets, theplurality of candidate digital images being selected using a relevancyscore associated with each candidate digital image of the plurality;analyzing, via the computing device, each candidate digital image todetect an object of a type corresponding to the entity; determining, viathe computing device, an n-dimensional feature vector for a candidatedigital image of the plurality using data of pixels corresponding to theobject detected in the candidate digital image, the feature vectordetermination being performed for each candidate digital image of theplurality to determine a plurality of feature vectors; forming, via thecomputing device, a plurality of clusters using the plurality of featurevectors, each cluster of the plurality comprising a number of featurevectors, each feature vector in each cluster corresponding to acandidate digital image of the plurality; and selecting, via thecomputing device, a set of candidate digital images for the set ofcanonical digital images using a number of clusters of the plurality,the candidate digital image selection comprising determining, for eachcandidate digital image with a corresponding feature vector belonging toa cluster of the number of clusters, a measure of quality based on atleast one consideration of quality, each candidate digital image of theset of candidate digital images having a higher measure of qualityrelative to the measure of quality associated with each unselectedcandidate digital image.

In accordance with one or more embodiments, a non-transitorycomputer-readable storage medium is provided, the non-transitorycomputer-readable storage medium tangibly storing thereon, or havingtangibly encoded thereon, computer readable instructions that whenexecuted cause at least one processor to perform a method forautomatically high quality, canonical digital images for an entity.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code (or program logic) executed by aprocessor(s) of a computing device to implement functionality inaccordance with one or more such embodiments is embodied in, by and/oron a non-transitory computer-readable medium.

DRAWINGS

The above-mentioned features and objects of the present disclosure willbecome more apparent with reference to the following description takenin conjunction with the accompanying drawings wherein like referencenumerals denote like elements and in which:

FIG. 1 is a schematic diagram illustrating an example of a networkwithin which the systems and methods disclosed herein could beimplemented according to some embodiments of the present disclosure;

FIG. 2 depicts is a schematic diagram illustrating an example of clientdevice in accordance with some embodiments of the present disclosure;

FIG. 3 is a schematic block diagram illustrating components of anexemplary system in accordance with embodiments of the presentdisclosure;

FIG. 4, which comprises FIGS. 4A and 4B, is a flowchart illustratingsteps performed in accordance with some embodiments of the presentdisclosure;

FIG. 5 provides a pictorial illustration of an input graph and clustersfrom the input in accordance with one or more embodiments of the presentdisclosure; and

FIG. 6 is a block diagram illustrating the architecture of an exemplaryhardware device in accordance with one or more embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments.Subject matter may, however, be embodied in a variety of different formsand, therefore, covered or claimed subject matter is intended to beconstrued as not being limited to any example embodiments set forthherein; example embodiments are provided merely to be illustrative.Likewise, a reasonably broad scope for claimed or covered subject matteris intended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium may comprisecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers may vary widely inconfiguration or capabilities, but generally a server may include one ormore central processing units and memory. A server may also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther include a system of terminals, gateways, routers, or the likecoupled by wireless radio links, or the like, which may move freely,randomly or organize themselves arbitrarily, such that network topologymay change, at times even rapidly.

A wireless network may further employ a plurality of network accesstechnologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G)cellular technology, or the like. Network access technologies may enablewide area coverage for devices, such as client devices with varyingdegrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers may vary widely in configuration or capabilities,but generally a server may include one or more central processing unitsand memory. A server may also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicemay include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device may, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device an Near Field Communication (NFC)device, a Personal Digital Assistant (PDA), a handheld computer, atablet computer, a phablet, a laptop computer, a set top box, a wearablecomputer, smart watch, an integrated or distributed device combiningvarious features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a simple smart phone, phablet or tablet mayinclude a numeric keypad or a display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude a high resolution screen, one or more physical or virtualkeyboards, mass storage, one or more accelerometers, one or moregyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch-sensitive color 2D or 3D display, forexample.

A client device may include or may execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device may include or may execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo!® Mail, short messageservice (SMS), or multimedia message service (MMS), for example Yahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram™, to provide only a few possibleexamples. A client device may also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device may also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing or displaying various forms of content, includinglocally stored or streamed video, or games (such as fantasy sportsleagues). The foregoing is provided to illustrate that claimed subjectmatter is intended to include a wide range of possible features orcapabilities.

The detailed description provided herein is not intended as an extensiveor detailed discussion of known concepts, and as such, details that areknown generally to those of ordinary skill in the relevant art may havebeen omitted or may be handled in summary fashion.

The principles described herein may be embodied in many different forms.By way of background, digital images can be associated with informationin a textual format, which is usually referred to as metadata. Metadatacan be embedded in a digital image file, together with the imagecontent, e.g., pixel data, or it can be contained in a separate fileassociated with the digital image file. Some metadata may beautomatically generated and some metadata may be added, or edited,manually using a software application, such as client application andserver application, for example. Some examples of the metadataassociated with a digital image include without limitation image capturedevice information (e.g., make and model of device, geographic locationin a case that the device includes a geographic positioning componentsuch as a Global Positioning System (GPS) device), exposure information,and descriptive information. The descriptive information can includewithout limitation keywords or phrases about the content.

A search engine can use the term(s) of a search query to search themetadata associated with digital images in much the same way that ituses a search query to search a corpus of documents containing text. Insuch a case, the search engine can return a number of digital imageswhose metadata is determined to be relevant to the search query term(s).Such an approach fails to consider the digital content itself. Forexample, consider a search using the terms “Brad Pitt”, the conventionalsearch engine is likely to return a number of digital images of theactor; however, the user may have been searching for digital images ofthe boxer name Brad Pitt. Even if the user added the term “boxer” to thesearch query, the search results can still include images of the actor.In addition to a problem of disambiguation, such a conventionalapproach's focus on the metadata does not consider the quality of BradPitt's depiction in a given digital image. Consequently, a digital imagewhose metadata is textually relevant to a search query may be returnedas a search result in place of another digital image that is morerepresentative of Brad Pitt, and is of higher quality.

There are millions of digital images available on the web and moredigital images are being added all the time. It is impossible for ahuman to attempt to review every digital image depicting an entity(e.g., person, point of interest, or other type of object) and select anumber of digital images that are most representative of the entity. Infact and given the number of digital images that are currently availableand the number of digital images that are being added, it is impossiblefor a computing system to use a supervised approach for selectingdigital images most representative of an entity, since a supervisedapproach requires that each digital image included in a training dataset for an entity have a tag, or other label, identifying the entity.

As such, the instant disclosure provides a novel solution addressing aneed for selecting a diverse set of high quality, canonical (or mostdefinitive, most representative, etc.) digital images for a given entitythat is scalable to any size corpus of digital images. While the presentdisclosure is discussed using “person” as one example of a type ofentity (indeed, celebrity queries can constitute a large part of thequery volume in digital image searching), or type of object, embodimentsof the present disclosure may be used to select canonical digital imagesfor any type of object depicted in a digital image.

The present disclosure provides novel systems and methods for automaticselection of high quality, canonical digital images from a corpus ofdigital images. The novel systems and methods are well suited for anyscale or size corpus, including a scale the size of the web, from whichselection of high quality, canonical digital images for each of a numberof entities can be selected. According to some embodiments, thedisclosed systems and methods use an unsupervised approach that uses acombination of metadata and visual features without manual or humanintervention, e.g., manual or human labeling of training data. Usingvisual features, diversity for the retrieved canonical digital imagescan be achieved across visual attributes and looks, such as age,hairstyles, etc.

According to some embodiments, the disclosed systems and methods firstsearches for digital images using a number of text-based searches andthe metadata associated with the digital images in a corpus, selects afirst set of candidate digital images comprising digital images returnedfrom each search, e.g., selects a number, n, of the most relevantdigital images in each search. The pixel data in each selected digitalimage (each candidate digital image) is analyzed. In one example, anarea, or portion, of a candidate image corresponding to a depiction ofan entity in the candidate digital image. A bounding box encompassingthe entity can be used to define the area and pixels corresponding tothe entity. In a case of a person entity, a face detection algorithm,such as Viola-Jones face detector, can be used to detect a face objectand define a bounding box including the detected face object.

The Viola-Jones face detector is trained using labeled training datawhich comprises a number of digital images depicting faces (and labeledas such) and a number of digital images not depicting faces (and labeledas such). An object detector, such as the Viola-Jones face detector, canmake use of a cascade of feature detectors, e.g., Haar feature-basedcascade of classifiers, to detect a type of object, such as a faceobject, depicted in a digital image.

After a face is detected in a candidate digital image, fiducial pointsare detected on the face. Some examples of fiducial points are, withoutlimitation, the corners of the eyes and mouth, centers of each eye andthe mouth, tip of the nose, eyebrows, chin, or the like. A number of thedetected fiducial points (e.g., 61 of 76 fiducial points detected usingthe Viola-Jones face detector) are selected to generate a feature vectorusing a number of pixel data regions including each selected fiducialpoint. While all of the fiducial points that are detected may be used,certain fiducial points that might be considered to be less importantthan other fiducial points might be discarded in order to reduce thesize of the resulting feature vector. By way of a one non-limitingexample, fiducial points located on the outer portion of a face might beconsidered to be less important than fiducial points involving the eyes,mouth, nose, eyebrows, or the like.

As is described in more detail below, a feature vector is created torepresent each candidate digital image identified as being textuallyrelevant to the entity, as determined using the search queries andassociated metadata. The feature vectors associated with the candidatedigital images are used to determine visual relevance to the entity. Inat least one embodiment, a clustering mechanism, such as and withoutlimitation Markov Cluster (MCL), or MCL algorithm, is used to formclusters, or groupings, of the feature vectors representing thecandidate digital images.

The feature vector created for each candidate digital image representsvisual cues of the candidate digital image which can be used invalidating whether or not the candidate digital image is a canonicaldigital image of an entity. The visual cues represented by eachcandidate digital image's feature vector can be used in the validationprocess using an unsupervised clustering approach in accordance with oneor more embodiments disclosed herein. In so doing, “noisy” images, e.g.,images that depict the wrong entity or digital images without facesaltogether, may be eliminated. Eliminating “noisy” digital imageimproves the relevance of digital images retrieved in response to arequest for digital images of a given entity.

To further illustrate using MCL as an example, input to MCL includes agraph comprising a node for each candidate digital image and weightededges connecting a node to each other node in the graph. Each node inthe input graph can be represented by the corresponding candidatedigital image's feature vector. A weight associated with an edgeconnecting two nodes represents the similarity between the two nodes asdetermined using the feature vectors of the two nodes. In one example,the similarity can be determined using a cosine-similarity of the twofeature vectors. In accordance with at least one embodiment, asimilarity that is less than a threshold similarity, e.g., less than a0.4 threshold value, can be set to zero.

Output of the MCL algorithm is a set of clusters formed using the MCLalgorithm and the input to the MCL algorithm, including the input graphand feature vectors of the candidate digital images. The clusters can beanalyzed to filter out outlier clusters, e.g., clusters with less than athreshold number of candidate digital images, so that some number of theoutput clusters considered to be the larger clusters remain. A number ofcandidate digital images are selected from each cluster, oralternatively from the larger clusters. As is described in more detailbelow, within a given one of the larger clusters, a number of groups ofcandidate digital images is formed. Within each group, candidate digitalimages are ranked based on quality and at least one top-ranked (from thequality-based ranking) candidate digital image is selected as acanonical digital image from the group. As is discussed in more detailbelow, a score (a “canonicalness” score) can be determined based on atleast one consideration (or criterion) of quality, such as entityproportion relative to other parts of the candidate digital image,entity location within the candidate digital image, and imageaesthetics.

Canonical digital image selection is performed for each group withineach cluster. In so doing, a diverse set of definitive, or canonical,images for the entity is determined. The set of canonical digital imagescan be associated with a number of terms, e.g., the terms used inselecting the candidate digital images. The set of terms associated witha set of canonical digital images can be used in identifying the set ofcanonical digital images in response to a search including one or moreterms form the set of associated terms. As one non-limiting, thecanonical digital images can be retrieved and distributed over the webusing a search engine, such as Yahoo!® Image Search, an online photosharing system, such as Flickr®, an online social networking system, orthe like.

Some of the benefits of the disclosed systems and methods can beevidenced two-fold: 1) the disclosed systems and methods provide atechnologically based mechanism for automatic selection of qualitycanonical digital images of an entity; and (2) the disclosed systems andmethods improve the efficiency and accuracy of existing digital imageretrieval and distribution technological systems by automaticallyselecting (from a corpus of digital images including low quality andless-representative depictions of an entity) a number of the highestquality, most representative digital images of an entity.

The disclosed systems and methods can be implemented for any type ofcontent item, including, but not limited to, video, audio, images, text,and/or any other type of multimedia content. While the discussion hereinwill focus on still digital image content items, it should not beconstrued as limiting, as any type of content or multimedia content,whether known or to be known, can be utilized without departing from thescope of the instant disclosure. By way of an example, digital videocontent can comprise a number of frames, each of which can be consideredto be a digital image comprising a number of pixels represented asbinary data.

Certain embodiments will now be described in greater detail withreference to the figures. In general, with reference to FIG. 1, a system100 in accordance with an embodiment of the present disclosure is shown.FIG. 1 shows components of a general environment in which the systemsand methods discussed herein may be practiced. Not all the componentsmay be required to practice the disclosure, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105, wireless network 110, mobile devices (clientdevices) 102-104 and client device 101. FIG. 1 additionally includes avariety of servers, such as content server 106, application (or “App”)server 108, search server 120 and advertising (“ad”) server 130.

One embodiment of mobile devices 102-104 is described in more detailbelow. Generally, however, mobile devices 102-104 may include virtuallyany portable computing device capable of receiving and sending a messageover a network, such as network 105, wireless network 110, or the like.Mobile devices 102-104 may also be described generally as client devicesthat are configured to be portable. Thus, mobile devices 102-104 mayinclude virtually any portable computing device capable of connecting toanother computing device and receiving information. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices 102-104 typically range widely interms of capabilities and features. For example, a cell phone may have anumeric keypad and a few lines of monochrome LCD display on which onlytext may be displayed. In another example, a web-enabled mobile devicemay have a touch sensitive screen, a stylus, and an HD display in whichboth text and graphics may be displayed.

A web-enabled mobile device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message.

Mobile devices 102-104 also may include at least one client applicationthat is configured to receive content from another computing device. Theclient application may include a capability to provide and receivetextual content, graphical content, audio content, and the like. Theclient application may further provide information that identifiesitself, including a type, capability, name, and the like. In oneembodiment, mobile devices 102-104 may uniquely identify themselvesthrough any of a variety of mechanisms, including a phone number, MobileIdentification Number (MIN), an electronic serial number (ESN), or othermobile device identifier.

In some embodiments, mobile devices 102-104 may also communicate withnon-mobile client devices, such as client device 101, or the like. Inone embodiment, such communications may include sending and/or receivingmessages, searching for, viewing and/or sharing photographs, audioclips, video clips, or any of a variety of other forms ofcommunications. Client device 101 may include virtually any computingdevice capable of communicating over a network to send and receiveinformation. The set of such devices may include devices that typicallyconnect using a wired or wireless communications medium such as personalcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, or the like. Thus, client device 101may also have differing capabilities for displaying navigable views ofinformation.

Client devices 101-104 may be capable of sending or receiving signals,such as via a wired or wireless network, or may be capable of processingor storing signals, such as in memory as physical memory states, andmay, therefore, operate as a server. Thus, devices capable of operatingas a server may include, as examples, dedicated rack-mounted servers,desktop computers, laptop computers, set top boxes, integrated devicescombining various features, such as two or more features of theforegoing devices, or the like.

Wireless network 110 is configured to couple mobile devices 102-104 andits components with network 105. Wireless network 110 may include any ofa variety of wireless sub-networks that may further overlay stand-alonead-hoc networks, and the like, to provide an infrastructure-orientedconnection for mobile devices 102-104. Such sub-networks may includemesh networks, Wireless LAN (WLAN) networks, cellular networks, and thelike.

Network 105 is configured to couple content server 106, applicationserver 108, or the like, with other computing devices, including, clientdevice 101, and through wireless network 110 to mobile devices 102-104.Network 105 is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 105 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, other forms of computer-readable media,or any combination thereof. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another,and/or other computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, may be compatible with or compliant withone or more protocols. Signaling formats or protocols employed mayinclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUI, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) may include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets may be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet may, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet may be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetmay, for example, be routed via a path of gateways, servers, etc. thatmay route the signal packet in accordance with a target address andavailability of a network path to the target address.

According to some embodiments, the present disclosure may also beutilized within or accessible to an electronic social networking site. Asocial network refers generally to an electronic network of individuals,such as acquaintances, friends, family, colleagues, or co-workers,coupled via a communications network or via a variety of sub-networks.Potentially, additional relationships may subsequently be formed as aresult of social interaction via the communications network orsub-networks. In some embodiments, multi-modal communications may occurbetween members of the social network. Individuals within one or moresocial networks may interact or communication with other members of asocial network via a variety of devices. Multi-modal communicationtechnologies refers to a set of technologies that permit interoperablecommunication across multiple devices or platforms, such as cell phones,smart phones, tablet computing devices, phablets, personal computers,televisions, set-top boxes, SMS/MMS, email, instant messenger clients,forums, social networking sites, or the like.

In some embodiments, the disclosed networks 110 and/or 105 may comprisea content distribution network(s). A “content delivery network” or“content distribution network” (CDN) generally refers to a distributedcontent delivery system that comprises a collection of computers orcomputing devices linked by a network or networks. A CDN may employsoftware, systems, protocols or techniques to facilitate variousservices, such as storage, caching, communication of content, orstreaming media or applications. A CDN may also enable an entity tooperate or manage another's site infrastructure, in whole or in part.

The content server 106 may include a device that includes aconfiguration to provide content via a network to another device. Acontent server 106 may, for example, host a site or service, such asstreaming media site/service (e.g., YouTube®), an email platform orsocial networking site, or a personal user site (such as a blog, vlog,online dating site, and the like). A content server 106 may also host avariety of other sites, including, but not limited to business sites,educational sites, dictionary sites, encyclopedia sites, wikis,financial sites, government sites, and the like. Devices that mayoperate as content server 106 include personal computers desktopcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, servers, and the like.

Content server 106 can further provide a variety of services thatinclude, but are not limited to, streaming and/or downloading mediaservices, search services, email services, photo services, web services,social networking services, news services, third-party services, audioservices, video services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, or the like.Such services, for example a video application and/or video platform,can be provided via the application server 108, whereby a user is ableto utilize such service upon the user being authenticated, verified oridentified by the service. Examples of content may include images, text,audio, video, or the like, which may be processed in the form ofphysical signals, such as electrical signals, for example, or may bestored in memory, as physical states, for example.

An ad server 130 comprises a server that stores online advertisementsfor presentation to users. “Ad serving” refers to methods used to placeonline advertisements on websites, in applications, or other placeswhere users are more likely to see them, such as during an onlinesession or during computing platform use, for example. Variousmonetization techniques or models may be used in connection withsponsored advertising, including advertising associated with user. Suchsponsored advertising includes monetization techniques includingsponsored search advertising, non-sponsored search advertising,guaranteed and non-guaranteed delivery advertising, adnetworks/exchanges, ad targeting, ad serving and ad analytics. Suchsystems can incorporate near instantaneous auctions of ad placementopportunities during web page creation, (in some cases in less than 500milliseconds) with higher quality ad placement opportunities resultingin higher revenues per ad. That is advertisers will pay higheradvertising rates when they believe their ads are being placed in oralong with highly relevant content that is being presented to users.Reductions in the time needed to quantify a high quality ad placementoffers ad platforms competitive advantages. Thus higher speeds and morerelevant context detection improve these technological fields.

For example, a process of buying or selling online advertisements mayinvolve a number of different entities, including advertisers,publishers, agencies, networks, or developers. To simplify this process,organization systems called “ad exchanges” may associate advertisers orpublishers, such as via a platform to facilitate buying or selling ofonline advertisement inventory from multiple ad networks. “Ad networks”refers to aggregation of ad space supply from publishers, such as forprovision en masse to advertisers. For web portals like Yahoo! ®,advertisements may be displayed on web pages or in apps resulting from auser-defined search based at least in part upon one or more searchterms. Advertising may be beneficial to users, advertisers or webportals if displayed advertisements are relevant to interests of one ormore users. Thus, a variety of techniques have been developed to inferuser interest, user intent or to subsequently target relevantadvertising to users. One approach to presenting targeted advertisementsincludes employing demographic characteristics (e.g., age, income, sex,occupation, etc.) for predicting user behavior, such as by group.Advertisements may be presented to users in a targeted audience based atleast in part upon predicted user behavior(s).

Another approach includes profile-type ad targeting. In this approach,user profiles specific to a user may be generated to model userbehavior, for example, by tracking a user's path through a web site ornetwork of sites, and compiling a profile based at least in part onpages or advertisements ultimately delivered. A correlation may beidentified, such as for user purchases, for example. An identifiedcorrelation may be used to target potential purchasers by targetingcontent or advertisements to particular users. During presentation ofadvertisements, a presentation system may collect descriptive contentabout types of advertisements presented to users. A broad range ofdescriptive content may be gathered, including content specific to anadvertising presentation system. Advertising analytics gathered may betransmitted to locations remote to an advertising presentation systemfor storage or for further evaluation. Where advertising analyticstransmittal is not immediately available, gathered advertising analyticsmay be stored by an advertising presentation system until transmittal ofthose advertising analytics becomes available.

Servers 106, 108, 120 and 130 may be capable of sending or receivingsignals, such as via a wired or wireless network, or may be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server may include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers may vary widely in configuration or capabilities, but generally,a server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 108, 120 and/or 130. This may include in a non-limitingexample, authentication servers, search servers, email servers, socialnetworking services servers, SMS servers, IM servers, MMS servers,exchange servers, photo-sharing services servers, and travel servicesservers, via the network 105 using their various devices 101-104. Insome embodiments, applications, such as a streaming video application(e.g., YouTube®, Netflix®, Hulu®, iTunes®, Amazon Prime®, HBO Go®, andthe like), blog, photo storage/sharing application or social networkingapplication (e.g., Flickr®, Tumblr®, and the like), can be hosted by theapplication server 108 (or content server 106, search server 120 and thelike). Thus, the application server 108 can store various types ofapplications and application related information including applicationdata and user profile information (e.g., identifying and behavioralinformation associated with a user). It should also be understood thatcontent server 106 can also store various types of data related to thecontent and services provided by content server 106 in an associatedcontent database 107, as discussed in more detail below. Embodimentsexist where the network 105 is also coupled with/connected to a TrustedSearch Server (TSS) which can be utilized to render content inaccordance with the embodiments discussed herein. Embodiments existwhere the TSS functionality can be embodied within servers 16, 18, 120and/or 130.

Moreover, although FIG. 1 illustrates servers 106, 108, 120 and 130 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 108, 120and/or 130 may be distributed across one or more distinct computingdevices. Moreover, in one embodiment, servers 106, 108, 120 and/or 130may be integrated into a single computing device, without departing fromthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating a client device showing anexample embodiment of a client device that may be used within thepresent disclosure. Client device 200 may include many more or lesscomponents than those shown in FIG. 2. However, the components shown aresufficient to disclose an illustrative embodiment for implementing thepresent disclosure. Client device 200 may represent, for example, clientdevices discussed above in relation to FIG. 1.

As shown in the figure, client device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224. Clientdevice 200 also includes a power supply 226, one or more networkinterfaces 250, an audio interface 252, a display 254, a keypad 256, anilluminator 258, an input/output interface 260, a haptic interface 262,an optional global positioning systems (GPS) receiver 264 and acamera(s) or other optical, thermal or electromagnetic sensors 266.Device 200 can include one camera/sensor 266, or a plurality ofcameras/sensors 266, as understood by those of skill in the art. Thepositioning of the camera(s)/sensor(s) 266 on device 200 can change perdevice 200 model, per device 200 capabilities, and the like, or somecombination thereof.

Power supply 226 provides power to client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power mayalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 252 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 252 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action. Display 254 may be a liquid crystal display (LCD), gasplasma, light emitting diode (LED), or any other type of display usedwith a computing device. Display 254 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 256 may comprise any input device arranged to receive input froma user. For example, keypad 256 may include a push button numeric dial,or a keyboard. Keypad 256 may also include command buttons that areassociated with selecting and sending images. Illuminator 258 mayprovide a status indication and/or provide light. Illuminator 258 mayremain active for specific periods of time or in response to events. Forexample, when illuminator 258 is active, it may backlight the buttons onkeypad 256 and stay on while the client device is powered. Also,illuminator 258 may backlight these buttons in various patterns whenparticular actions are performed, such as dialing another client device.Illuminator 258 may also cause light sources positioned within atransparent or translucent case of the client device to illuminate inresponse to actions.

Client device 200 also comprises input/output interface 260 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like. Haptic interface 262 is arranged to providetactile feedback to a user of the client device. For example, the hapticinterface may be employed to vibrate client device 200 in a particularway when the client device 200 receives a communication from anotheruser.

Optional GPS transceiver 264 can determine the physical coordinates ofclient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 264 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or thelike, to further determine the physical location of client device 200 onthe surface of the Earth. It is understood that under differentconditions, GPS transceiver 264 can determine a physical location withinmillimeters for client device 200; and in other cases, the determinedphysical location may be less precise, such as within a meter orsignificantly greater distances. In one embodiment, however, clientdevice may through other components, provide other information that maybe employed to determine a physical location of the device, includingfor example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of client device 200. The mass memory also stores an operatingsystem 241 for controlling the operation of client device 200. It willbe appreciated that this component may include a general purposeoperating system such as a version of UNIX, or LINUX™, or a specializedclient communication operating system such as Windows Client™, or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 230 further includes one or more data stores, which can beutilized by client device 200 to store, among other things, applications242 and/or other data. For example, data stores may be employed to storeinformation that describes various capabilities of client device 200.The information may then be provided to another device based on any of avariety of events, including being sent as part of a header during acommunication, sent upon request, or the like. At least a portion of thecapability information may also be stored on a disk drive or otherstorage medium (not shown) within client device 200.

Applications 242 may include computer executable instructions which,when executed by client device 200, transmit, receive, and/or otherwiseprocess audio, video, images, and enable telecommunication with a serverand/or another user of another client device. Other examples ofapplication programs or “apps” in some embodiments include browsers,calendars, contact managers, task managers, transcoders, photomanagement, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 242 may further include search client 245 that isconfigured to send, to receive, and/or to otherwise process a searchquery and/or search result using any known or to be known communicationprotocols. Although a single search client 245 is illustrated it shouldbe clear that multiple search clients may be employed. For example, onesearch client may be configured to enter a search query message, whereanother search client manages search results, and yet another searchclient is configured to manage serving advertisements, IMs, emails, andother types of known messages, or the like.

Having described the components of the general architecture employedwithin the disclosed systems and methods, the components' generaloperation with respect to the disclosed systems and methods will now bedescribed below.

FIG. 3 is a block diagram illustrating the components for performing thesystems and methods discussed herein. FIG. 1 includes a digital imageselection engine 300, network 310 and database 320. The image selectionengine 300 can be a special purpose machine or processor and could behosted by an application server, content server, social networkingserver, web server, search server, content provider, email serviceprovider, ad server, user's computing device, and the like, or anycombination thereof.

According to some embodiments, image selection engine 300 can beembodied as a stand-alone application that executes on a user device. Insome embodiments, the image selection engine 300 can function as anapplication installed on the user's device, and in some embodiments,such application can be a web-based application accessed by the userdevice over a network. In some embodiments, the image selection engine300 can be installed as an augmenting script, program or application toanother media application (e.g., Yahoo! ® Image Search, Flickr®, and thelike).

The database 320 can be any type of database or memory, and can beassociated with a content server on a network (such as and withoutlimitation a content server, search server, application server, etc.,)or a user's device. Database 320 comprises a dataset of data andmetadata associated with local and/or network information related tousers, services, applications, content (e.g., video) and the like. Suchinformation can be stored and indexed in the database 320 independentlyand/or as a linked or associated dataset. It should be understood thatthe data (and metadata) in the database 320 can be any type ofinformation and type, whether known or to be known, without departingfrom the scope of the present disclosure.

According to some embodiments, database 320 can store data for users,e.g., user data. According to some embodiments, the stored user data caninclude, but is not limited to, information associated with a user'sprofile, user interests, user behavioral information, user attributes,user preferences or settings, user demographic information, userlocation information, user biographic information, and the like, or somecombination thereof. In some embodiments, the user data can alsoinclude, for purposes creating, recommending, rendering and/ordelivering GIFs or videos, user device information, including, but notlimited to, device identifying information, device capabilityinformation, voice/data carrier information, Internet Protocol (IP)address, applications installed or capable of being installed orexecuted on such device, and/or any, or some combination thereof. Itshould be understood that the data (and metadata) in the database 320can be any type of information related to a user, content, a device, anapplication, a service provider, a content provider, whether known or tobe known, without departing from the scope of the present disclosure.

According to some embodiments, database 320 can store data and metadataassociated with digital image content from an assortment of mediaproviders. For example, the information can be related to, but notlimited to, metadata such as that described herein and pixel data foreach digital image content item. As discussed above, the metadata can beautomatically generated by an image capture device and/or provided bythe user using a client application and/or using a web-based applicationprovided by a content/service provider (i.e., Yahoo!®, Flickr® orTumblr®), a social networking provider or other third party services(e.g., Facebook®, Twitter® and the like), or some combination thereof.

According to some embodiments, each digital image (e.g., each candidatedigital image) can be represented as an n-dimensional vector (or featurevector), and each such feature vector can be stored in database 320,together with an association relating a digital image to its featurevector. In addition, database 320 can store a mapping between a set ofcanonical digital images and a set of associated search terms. Database320 may also store a “canonicalness” score associated with a canonicaldigital image.

While the discussion below will involve cluster analysis of digitalimages, as discussed above, the information can be analyzed, stored andindexed according to any known or to be known computational analysistechnique or algorithm, such as, but not limited to, data mining,Bayesian network analysis, Hidden Markov models, artificial neuralnetwork analysis, logical model and/or tree analysis, and the like.

The network 310 can be any type of network such as, but not limited to,a wireless network, a local area network (LAN), wide area network (WAN),the Internet, or a combination thereof. The network 110 facilitatesconnectivity of the image selection engine 300, and the database ofstored resources 320. Indeed, as illustrated in FIG. 1, the imageselection engine 300 and database 320 can be directly connected by anyknown or to be known method of connecting and/or enabling communicationbetween such devices and resources.

The principal processor, server, or combination of devices thatcomprises hardware programmed in accordance with the special purposefunctions herein is referred to for convenience as digital image engine300, and includes text-based (digital image) selection module 302,object feature detection module 304, clustering module 306, and(canonical digital image) selection module 308. It should be understoodthat the engine(s) and modules discussed herein are non-exhaustive, asadditional or fewer engines and/or modules (or sub-modules) may beapplicable to the embodiments of the systems and methods discussed. Theoperations, configurations and functionalities of each module, and theirrole within embodiments of the present disclosure will be discussed withreference to FIG. 4.

As discussed in more detail below, the information processed by theimage selection engine 300 can be supplied to the database 320 in orderto ensure that the information housed in the database 320 is up-to-dateas the disclosed systems and methods leverage real-time informationand/or behavior associated with a digital image file, user and/or theuser's device during or responsive to image selection and retrieval, asdiscussed in more detail below.

FIG. 4, which comprises FIGS. 4A and 4B, provides a process flowoverview in accordance with one or more embodiments of the presentdisclosure. Process 400 of FIG. 4 details steps performed in accordancewith exemplary embodiments of the present disclosure for automaticallyselecting a number of high quality, canonical digital images depicting,and representative of, each entity. According to some embodiments, asdiscussed herein with relation to FIG. 4, the process involvesautomatically selecting high quality, canonical digital imagesrepresentative of, and depicting, an entity, or entities. Such selectioninvolves selection of a set of candidate digital images based on textualrelevance, detecting features of a given entity (or object) in eachtextually-relevant candidate digital image detection, clustering thecandidate digital images to form a number of digital image groups, andselecting a number of the candidate digital images as high quality,canonical digital images for the given entity, as is described in moredetail below.

In the example of FIG. 4, steps 404-408 can be executed in text-baseddigital image selection module 302, steps 410-412 can be executed inobject feature detection module 304, step 414 can be executed inclustering module 306 and step 416 can be executed in selection module308.

At step 402, an entity for which a number of high quality, canonicaldigital images is to be determined is received by image selection engine300. In one example, the image selection engine 300 may operate in anonline mode, and a request for retrieval of a set of digital images foran entity might be received from a user or from an image search andretrieval system in response to a request received by the image searchand retrieval system from a user. In another example, image selectionengine 300 may operate in an offline mode, and process a number ofentities, each with associated search terms, and identify, for eachentity, a number of high quality, canonical digital images.

In any case, each entity has a number of search terms associated withthe entity. The search terms may be received as part of a request.Alternatively, the search terms can be derived from information aboutthe entity. In one example, the entity has some initial information,such as a name, descriptive title, unique identifier from a referencesource, such as and without limitation Wikipedia® (e.g., a Wikipedia®ID), etc. This initial information may be used to expand the initialinformation to form a number of queries at step 504. Each query includesa number of attributes of the entity for disambiguation purposes. In theexample discussed above, the attribute “boxer” can be included with“Brad Pitt” in a search query for the boxer person entity, oralternatively the attribute “actor” can be included with “Brad Pitt” ina search query for the actor person entity. Other attributes may beincluded, such as and without limitation “young”, “Oscars”, etc. forBrad Pitt the actor, for example.

At step 406, each query is submitted to a search engine to search atleast one corpus of digital images. Each set of search results for agiven query includes a number of result items, each result itemidentifying a corresponding digital image. In addition, each result itemhas an associated rank (relative to other result items in the same setof search results) and a relevancy score determined by the searchengine. A result item's rank and relevancy score can be used as anindicator of the corresponding digital image's textual relevance to thesearch query (used to generate the set of search results) and to theentity.

Each set of search results and each result item's ranking and relevancyscore in the set of search results are passed to step 408 to select anumber of digital images (textually relevant to the entity) from eachset of search results based on their respective ranking and relevance inthe set of search results. More particularly and at step 408, for eachset of search results, a number (e.g., 50) of the top-ranked (mostrelevant) digital images are selected and added to a set of candidatedigital images that are processed in succeeding steps to select, usingan unsupervised approach, a number of high quality, canonical (ordefinitive) digital images for the entity. In this process, a set ofhigh quality, canonical digital images is determined, each of which isboth textually and visually relevant to the entity and determined to beof higher quality than each of the unselected candidate digital images.

In the example given above in connection with step 408, the number ofcandidate digital images selected from a set of search results is givenas 50. It should be apparent that any number of candidate digital imagesmay be selected from each set of search results, and that the number ofsearch results selected from each set of search results may vary fromone set of search result to another.

The pixel data in each selected candidate digital image is analyzed. Atstep 410, each candidate digital image from the set of candidate digitalimages is analyzed to detect a type of object corresponding to the typeof the entity in the candidate digital image. In one example inconnection with a person entity, a number of face detectors can be usedto “locate” a face object in the candidate digital image. Continuing theexample using a person entity, one example of a face object detector isthe Viola-Jones face detector which detects a number of fiducial pointson a face. Another face detector, such as Active Shape Models WithStasm, may be used instead of, or in addition to, the Viola-Jones facedetector. A different object detector, or object detectors, may be usedfor each different object, or entity, type. Any object detector nowknown or later developed can be used in connection with embodiments ofthe present disclosure.

An area, or areas, of a candidate digital image including the same typeof object as the entity is identified using one or more objectdetectors. A bounding box encompassing the entity can be used toidentify one area. An area's corresponding bounding box can be definedusing the locations of the fiducial points. In one example, arectangular-shaped bounding box can be defined by x and y coordinates ofeach corner of area in the candidate digital image. Each corner of thebounding box can correspond to a pixel (and its location) in thecandidate digital image. The bounding box can be defined to encompassall, or a selected set of, fiducial points identified by an objectdetector. It should be apparent that any size or shape can be used as a“bounding box” for defining an area of a candidate digital imageincluding a detected object.

As discussed above, an object detector, such as the Viola-Jones facedetector, can identify a number of fiducial points of the object, e.g.,fiducial points of a person entity's face in a candidate digital image.Some examples of fiducial points of a face object are, withoutlimitation, the corners of the eyes and mouth, centers of each eye andthe mouth, tip of the nose, eyebrows, chin, or the like. A number of thedetected fiducial points (e.g., 61 of 76 fiducial points detected usingthe Viola-Jones face detector) are selected to generate a feature vectordetermined using the pixel data around each selected fiducial point.While all of the fiducial points that are detected may be used, certainfiducial points that might be considered to be less important than otherfiducial points might be discarded in order to reduce the size of theresulting feature vector. By way of a one non-limiting example, fiducialpoints located on the outer portion of a face might be considered to beless important than fiducial points involving the eyes, mouth, nose,eyebrows, or the like.

At step 412, a feature vector is created to represent each candidatedigital image. In generating the feature vector for a candidate digitalimage, descriptors are extracted around each fiducial point beingconsidered. In one example, a region of pixels, such as a 16×16 squarepixel region, is defined, and the pixel data within the region isprocessed to extract a number of feature descriptors. In one example,scale-invariant feature transform (SIFT) algorithm can be used to detectand define local features (or local feature descriptors) in thecandidate digital image. Histogram of oriented gradient (HOG) algorithmis another example of a feature descriptor algorithm (and correspondinglocal feature descriptor) which can be used instead of, or in additionto SIFT. It should be apparent that any feature descriptor algorithm nowknown or later developed can be used to determine a feature using pixeldata of a candidate digital image.

In at least one embodiment, a number of feature descriptors areidentified at different scales, or sizes, of pixel regions. A 16×16pixel region is one example of one pixel region scale, or size, whichmay be used. Other examples of pixel region scales, or sizes, for whicha number of feature descriptors can be generated include 32×32 and64×64. It should be apparent that any pixel region scale and any numberof scales (or pixel region sizes) may be used. In accordance with one ormore embodiments, three scales, e.g., 16×16, 32×32 and 64×64, are usedfor each fiducial point being considered (e.g., all or some subset ofthe fiducial points identified by an object detector), and the pixeldata in each corresponding pixel region is processed using a number offeature descriptor extractors to generate a set of feature descriptorsfor each fiducial point being considered.

To further illustrate, assume that 61 fiducial points are beingconsidered, and that three different pixel region scales and to twodifferent feature extractors are used to extract features for eachfiducial point. Using this scenario, each candidate digital image isrepresented by a feature vector comprising 366 features (or 61 fiducialpoints, each with two features extracted from each of three differentscales, or sizes, of pixel regions).

The extracted features can be aggregated, e.g., concatenated, resultingin an aggregate feature vector representing the candidate digital image.In accordance with one or more embodiments, a dimensionality reducer,such as principal component analysis (PCA), can be used to reduce thesize of the aggregate feature vector. In one example, a dimensionalityreducer such as PCA can be used to reduce the size of the aggregatefeature vector, or feature vector, to 5490. Of course, a feature vectorrepresenting a candidate digital image need not be reduced or it can bereduced to any desired size using PCA, or another dimensionalityreducer.

At step 414, a clustering mechanism, such as and without limitation theMCL algorithm, is used to form clusters, or groupings, of the candidatedigital images using the feature vectors representing the candidatedigital images. To further illustrate using the MCL algorithm as anexample, the input to the MCL algorithm is a graph comprising a node foreach candidate digital image and weighted edges connecting each node toeach other node in the graph.

FIG. 5 provides a pictorial illustration of an input graph and clustersfrom the input in accordance with one or more embodiments of the presentdisclosure. Example 500 is a pictorial illustration of a graph input toan MCL algorithm. As illustrated in example 500, the input graphincludes a number of nodes (shown as black circles, some of which arelabeled with reference number 502 for purposes of illustration) andedges (shown as lines, some of which are labeled using the referencenumber 504 for purposes of illustration) between two nodes. Each node inthe input graph can be represented by the corresponding candidatedigital image's feature vector. A weight associated with an edgeconnecting two nodes represents the similarity between the two nodes asdetermined using the feature vectors determined for the two candidatedigital images corresponding to the two nodes. In one example, thesimilarity can be determined using a cosine-similarity function and thetwo feature vectors determined for the two candidate digital images. Inaccordance with at least one embodiment, a similarity that is less thana threshold similarity, e.g., less than a 0.4 threshold value, can beset to zero. Output of the MCL algorithm is a set of clusters formedusing the input to the MCL algorithm. A number of clusters arepictorially shown in example 510; some of the clusters are labeled withthe reference number 514 for purposes of illustration. In example 510,each cluster comprises a number of nodes 512, each of which correspondsto a candidate digital image.

At step 416 of FIG. 4, a number of canonical digital images is selectedfrom each of a number of the clusters identified at step 412. Acanonical digital image that is selected is determined (relative toother candidate digital images considered and not selected) to bedefinitive, or most representative, of the entity and to be of highquality using functionality described herein. In accordance with atleast one embodiment, the clusters can be analyzed to filter out outlierclusters, e.g., clusters with less than a threshold number of candidatedigital images, so that some number of the output clusters considered tobe the larger clusters remain. A number of candidate digital images areselected from each of the larger clusters.

Within a given one of the larger clusters, a number of candidate digitalimage groups are formed. The candidate digital images within a givencluster can be grouped according to similarity, which can be defined bya similarity score determined using a cosine-similarity algorithm andthe feature vector associated with each candidate digital image. In oneexample, similar candidate digital images are used to form a group ofcandidate digital images.

In one example of a formation of a group of candidate digital imageswithin a cluster, a first candidate digital image in the cluster can beselected and a similarity score can be determined in connection with thefirst candidate digital image and each other candidate digital image (orsecond candidate digital image) in the cluster. In this example, thefeature vectors corresponding to the first candidate digital image and asecond candidate digital image can be input to a cosine-similarityalgorithm to determine a similarity score for the pair of candidatedigital images. In this example, a threshold similarity score, e.g.,threshold similarity score of 0.8, can be used to select any secondcandidate digital images that are sufficiently similar (e.g., have asimilarity score that is greater than 0.8) to the first candidatedigital image. The selected second candidate digital images and thefirst candidate digital image can form a group of similar candidatedigital images within a cluster.

Within each group, a “canonicalness” (or quality) score is determinedfor each candidate digital image. The score determined for a candidatedigital image is a function of at least one consideration of quality ofeach candidate digital image. Within a group, the candidate digitalimages belonging to the group are ranked based on each candidate digitalimage's canonicalness score relative to the score of each othercandidate digital image in the group. At least one top-ranked candidatedigital image can be selected as a high quality, canonical digital imagefrom each group.

In accordance with at least one embodiment, a “canonicalness” score canbe determined based on at least one consideration of quality, such asentity proportion relative to other parts of the candidate digitalimage, entity location within the candidate digital image, and imageaesthetics.

In one example in which the entity's proportion (or percentage) isdetermined relative to the candidate digital image, the bounding boxencompassing the entity's depiction in the candidate digital image canbe used to identify the number (or count) of the pixels corresponding tothe object (the entity) found in candidate digital image relative to atotal number of pixels in the candidate digital image. The proportion ofpixels corresponding to the detected entity can be compared to athreshold proportion (or percentage) to determine whether or not theentity's proportion is sufficient, and an entity proportion score can bebased on the determined proportion. In one example, the entityproportion score can be the determined proportion (or percentage).

Another example of a condition of quality of a candidate digital imagethat may be used involves a location of the entity's depiction in thecandidate digital image. In this example, the bounding box can be usedas an indicator of the location of the entity in the candidate digitalimage. In one example, the condition of quality based on location can bea larger value in a case that the bounding box is closer to the centerof the candidate digital image and smaller value in a case that thebounding box is farther from the center (e.g., toward a side, toward thebottom, etc.).

Another example of a condition of quality of a candidate digital imagethat may be used involves a determination, using a text object detector,whether or not the candidate digital image depicts text. If so, thecandidate digital image can be assigned a lower (relative to othercandidate digital images in which text is not detected) score for thisquality measure, or the candidate digital image can be eliminated fromconsideration altogether.

Another example of a condition of quality of a candidate digital imagethat may be used involves a determination whether or not the candidatedigital image is a natural image, e.g., an image taken by a camera orother digital image capturing device, as opposed to a sketch or acartoon. A neural network-based classifier may be used that ispre-trained using features of both natural as well as other images,which feature for a given training image may include a label indicatingwhether the candidate digital image is a natural image or other than anatural image, e.g., a sketch, cartoon, etc. By way of one non-limitingexample, a Flickr® classifier can be used to process the candidatedigital image to determine whether or not it is a natural image. Acandidate digital image that is determined to be a natural image isscored higher for this quality measure than a candidate digital imagethat is determined to not be a natural image.

Another example of a condition of quality of a candidate digital imagethat may be used involves the aesthetics of a candidate digital image. Aclassifier, such as a support vector machine, may be used to determine ameasure, or score, of the aesthetic quality of the candidate digitalimage. In one example, the classifier can be used to determine whetheror not the candidate digital image contains offensive content.

Where more than one quality condition is used, the quality scoresdetermined for each of the multiple quality conditions may beaggregated, e.g., summed, summed and then averaged, etc. Each qualityscore may have an associated weighting, so that one quality condition'sscore might be weighted, or considered, more heavily, or less heavily,in determining the aggregate quality score. The quality score, which caninclude, for example, one or more scores determined for one or more ofthe quality conditions discussed above, can be used as the candidatedigital image's “canonicalness” score.

As discussed above, canonical digital image selection is performed foreach grouping within each cluster, at step 416. In so doing, a diverseset of canonical digital images for the entity can be determined. Theset of canonical digital images can be associated (e.g., in database320) with a number of terms, e.g., the terms used in selecting thecandidate digital images. The set of terms associated with a set ofcanonical digital images can be used in identifying the set of canonicaldigital images in response to a search including one or more terms formthe set of associated terms. Each canonical digital image can also beassociated with its canonicalness score (e.g., in database 320), and thecanonical score associated with each canonical digital image for anentity can be used in ordering, or ranking, the canonical digital imagesin a presentation of the digital images for an entity, for example.

At step 418, the canonical digital images associated with the entityidentified in step 402 can be retrieved and distributed. In one example,an entity's canonical digital image, or images, can be retrieved anddistributed over the web via a search engine, such as Yahoo! ® ImageSearch, an online photo sharing system, such as Flickr®, an onlinesocial networking system, or the like. In one example, the searchengine, online phone sharing system, etc. can access database 320 toretrieve a set of canonical digital images for an entity using one ormore search terms associated with the entity and the canonical digitalimages in the set. The canonicalness score associated with eachcanonical digital image can be used in selecting a subset of thecanonical digital images associated with an entity, e.g., selecting thetop, n, scoring canonical digital images for a given entity.

In one example, embodiments of the present disclosure can be used topower a “celebrity timeline” feature of a web service, such asFastbreak®, which timeline can be communicated to a client computingdevice and displayed using a display of a client computing device. Thetimeline can display a number of canonical digital images of a person,e.g., a celebrity, at various ages, or age ranges. The automatedselection process, which uses an unsupervised approach in selectingcanonical digital images, can be used in place of a manual editorialprocess, which would not be able to consider the large number ofavailable images for one entity, let alone the large number of entity,e.g., people, points of interest, etc., that may be the basis of arequest received by such an online service.

Steps 404-416 may be performed for a large number of entities and forany size corpus of digital images. Steps 404-416 can be repeated asfrequently as desired to select a fresh set of canonical digital imagesbased on recency, so that the digital images recently added to a corpusare considered in determining a set of canonical digital images for eachof a number of entities.

As shown in FIG. 6, internal architecture 600 of a computing device(s),computing system, computing platform, user devices, set-top box, smartTV and the like includes one or more processing units, processors, orprocessing cores, (also referred to herein as CPUs) 612, which interfacewith at least one computer bus 602. Also interfacing with computer bus602 are computer-readable medium, or media, 606, network interface 614,memory 604, e.g., random access memory (RAM), run-time transient memory,read only memory (ROM), media disk drive interface 620 as an interfacefor a drive that can read and/or write to media including removablemedia such as floppy, CD-ROM, DVD, media, display interface 610 asinterface for a monitor or other display device, keyboard interface 616as interface for a keyboard, pointing device interface 618 as aninterface for a mouse or other pointing device, and miscellaneous otherinterfaces not shown individually, such as parallel and serial portinterfaces and a universal serial bus (USB) interface.

Memory 604 interfaces with computer bus 602 so as to provide informationstored in memory 604 to CPU 612 during execution of software programssuch as an operating system, application programs, device drivers, andsoftware modules that comprise program code, and/or computer executableprocess steps, incorporating functionality described herein, e.g., oneor more of process flows described herein. CPU 612 first loads computerexecutable process steps from storage, e.g., memory 604, computerreadable storage medium/media 606, removable media drive, and/or otherstorage device. CPU 612 can then execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 612during the execution of computer-executable process steps.

Persistent storage, e.g., medium/media 606, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

Network link 628 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 628 mayprovide a connection through local network 624 to a host computer 626 orto equipment operated by a Network or Internet Service Provider (ISP)630. ISP equipment in turn provides data communication services throughthe public, worldwide packet-switching communication network of networksnow commonly referred to as the Internet 632.

A computer called a server host 634 connected to the Internet 632 hostsa process that provides a service in response to information receivedover the Internet 632. For example, server host 634 hosts a process thatprovides information representing video data for presentation at display610. It is contemplated that the components of system 600 can bedeployed in various configurations within other computer systems, e.g.,host and server.

At least some embodiments of the present disclosure are related to theuse of computer system 600 for implementing some or all of thetechniques described herein. According to one embodiment, thosetechniques are performed by computer system 600 in response toprocessing unit 612 executing one or more sequences of one or moreprocessor instructions contained in memory 604. Such instructions, alsocalled computer instructions, software and program code, may be readinto memory 604 from another computer-readable medium 606 such asstorage device or network link. Execution of the sequences ofinstructions contained in memory 604 causes processing unit 612 toperform one or more of the method steps described herein. In alternativeembodiments, hardware, such as ASIC, may be used in place of or incombination with software. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link and other networks throughcommunications interface, carry information to and from computer system600. Computer system 600 can send and receive information, includingprogram code, through the networks, among others, through network linkand communications interface. In an example using the Internet, a serverhost transmits program code for a particular application, requested by amessage sent from computer, through Internet, ISP equipment, localnetwork and communications interface. The received code may be executedby processor 602 as it is received, or may be stored in memory 604 or instorage device or other non-volatile storage for later execution, orboth.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

The invention claimed is:
 1. A method comprising: receiving, at acomputing device, a request for a set of canonical digital images of anentity; generating, via the computing device, a number of digital imagesearch result sets, the search result set generation comprising queryinga number of digital image data stores using a number of queries, eachquery comprising a number of search terms; selecting, via the computingdevice, a plurality of candidate digital images from the number ofdigital image search result sets, the plurality of candidate digitalimages being selected using a relevancy score associated with eachcandidate digital image of the plurality; analyzing, via the computingdevice, each candidate digital image to detect an object of a typecorresponding to an object type of the entity and to detect a number offiducial points of, the object type, in the detected object;determining, via the computing device, an n-dimensional feature vectorfor a candidate digital image of the plurality using data of pixelscorresponding to the number of fiducial points of the object detected inthe candidate digital image, the feature vector determination beingperformed for each candidate digital image of the plurality to determinea plurality of feature vectors; forming, via the computing device, aplurality of clusters using the plurality of feature vectors, eachcluster of the plurality comprising a number of feature vectors, eachfeature vector in each cluster corresponding to a candidate digitalimage of the plurality; and selecting, via the computing device, a setof candidate digital images for the set of canonical digital imagesusing a number of clusters of the plurality, the candidate digital imageselection comprising determining, for each candidate digital image witha corresponding feature vector belonging to a cluster of the number ofclusters, a measure of quality based on at least one consideration ofquality, each candidate digital image of the set of candidate digitalimages having a higher measure of quality relative to the measure ofquality associated with each unselected candidate digital image.
 2. Themethod of claim 1, further comprising: communicating, via the computingdevice and to a client computing device over an electroniccommunications network, the set of canonical digital images of theentity for display at the client computing device.
 3. The method ofclaim 2, the request for the set of canonical digital images of theentity being received from the client computing device over theelectronic communications network.
 4. The method of claim 1, the featurevector determination further comprising: determining, via the computingdevice and for the candidate digital image of the plurality, a number offeature descriptors for each fiducial point of the number of fiducialpoints using a number of pixel regions, each feature descriptor beinggenerated by analyzing a pixel region of the number of pixel regionsusing a feature descriptor algorithm; and aggregating the number offeature descriptors to form the feature vector for the candidate digitalimage.
 5. The method of claim 4, the candidate digital image analysisfurther comprising: analyzing, via the computing device, a candidatedigital image of the plurality using a face detector algorithm to detecta plurality of fiducial points of the object, wherein the detectedobject is a face object; and selecting the number of fiducial points torepresent the face object.
 6. The method of claim 4, the featuredescriptor aggregation further comprising: reducing, via the computingdevice, the feature vector's dimensionality using a principal componentanalysis transformation.
 7. The method of claim 1, the cluster formationfurther comprising: forming, via the computing device, the plurality ofclusters using a Markov Cluster (MCL) and an input graph representingthe plurality of candidate digital images, the input graph comprising anode for each candidate digital image of the plurality and an edge foreach pair of candidate digital images of the plurality, the edge havingan associated edge weight that is based on a measure of similaritydetermined using the feature vectors corresponding to the candidatedigital images of the pair, input to the MCL for the node correspondingto a candidate digital image including the feature vector determined forthe candidate digital image.
 8. The method of claim 7, furthercomprising: setting, via the computing device, each edge weightdetermined to be less than a similarity to a predetermined minimum edgeweight.
 9. The method of claim 1, the canonical digital image setselection further comprising: grouping, via the computing device, thenumber of feature vectors in a given cluster of the number of clustersbased on similarity, the grouping forming a number of feature vectorgroups in the given cluster, the canonical digital image set selectioncomprising selecting the set of canonical digital images from each groupin the given cluster.
 10. The method of claim 1, the measure of quality,for a given candidate digital image, is at least based on a proportionof the pixels corresponding to the object detected in the givencandidate digital image relative to a total number of pixels of thecandidate digital image.
 11. The method of claim 1, the measure ofquality, for a given candidate digital image, is at least based on alocation of the object detected in the given candidate digital image.12. The method of claim 1, the measure of quality, for a given candidatedigital image, is at least based on a determination whether or not thecandidate digital image depicts text.
 13. The method of claim 1, themeasure of quality, for a given candidate digital image, is at leastbased on a determination whether or not the candidate digital image is anatural image.
 14. The method of claim 1, the measure of quality, for agiven candidate digital image, is at least based on a determination ofan aesthetic quality of the given candidate digital image.
 15. Anon-transitory computer-readable storage medium tangibly encoded withcomputer-executable instructions, that when executed by a processorassociated with a computing device, performs a method comprising:receiving a request for a set of canonical digital images of an entity;generating a number of digital image search result sets, the searchresult set generation comprising querying a number of digital image datastores using a number of queries, each query comprising a number ofsearch terms; selecting a plurality of candidate digital images from thenumber of digital image search result sets, the plurality of candidatedigital images being selected using a relevancy score associated witheach candidate digital image of the plurality; analyzing each candidatedigital image to detect an object of a type corresponding to an objecttype of the entity and to detect a number of fiducial points of, theobject type, in the detected object; determining an n-dimensionalfeature vector for a candidate digital image of the plurality using dataof pixels corresponding to the number of fiducial points of the objectdetected in the candidate digital image, the feature vectordetermination being performed for each candidate digital image of theplurality to determine a plurality of feature vectors; forming aplurality of clusters using the plurality of feature vectors, eachcluster of the plurality comprising a number of feature vectors, eachfeature vector in each cluster corresponding to a candidate digitalimage of the plurality; and selecting a set of candidate digital imagesfor the set of canonical digital images using a number of clusters ofthe plurality, the candidate digital image selection comprisingdetermining, for each candidate digital image with a correspondingfeature vector belonging to a cluster of the number of clusters, ameasure of quality based on at least one consideration of quality, eachcandidate digital image of the set of candidate digital images having ahigher measure of quality relative to the measure of quality associatedwith each unselected candidate digital image.
 16. The non-transitorycomputer-readable storage medium of claim 15, further comprising:communicating, via the computing device and to a client computing deviceover an electronic communications network, the set of canonical digitalimages of the entity for display at the client computing device.
 17. Thenon-transitory computer-readable storage medium of claim 15, the featurevector determination further comprising: determining, via the computingdevice and for the candidate digital image of the plurality, a number offeature descriptors for each fiducial point of the number of fiducialpoints using a number of pixel regions, each feature descriptor beinggenerated by analyzing a pixel region of the number of pixel regionsusing a feature descriptor algorithm; and aggregating the number offeature descriptors to form the feature vector for the candidate digitalimage.
 18. The non-transitory computer-readable storage medium of claim17, the candidate digital image analysis further comprising: analyzing,via the computing device, a candidate digital image of the pluralityusing a face detector algorithm to detect a plurality of fiducial pointsof the object, wherein the detected object is a face object; andselecting a number of fiducial points to represent the face object. 19.The non-transitory computer-readable storage medium of claim 15, thecluster formation further comprising: forming, via the computing device,the plurality of clusters using a Markov Cluster (MCL) and an inputgraph representing the plurality of candidate digital images, the inputgraph comprising a node for each candidate digital image of theplurality and an edge for each pair of candidate digital images of theplurality, the edge having an associated edge weight that is based on ameasure of similarity determined using the feature vectors correspondingto the candidate digital images of the pair, input to the MCL for thenode corresponding to a candidate digital image including the featurevector determined for the candidate digital image.
 20. A computingdevice comprising: a processor; a non-transitory storage medium fortangibly storing thereon program logic for execution by the processor,the program logic comprising: receiving logic executed by the processorfor receiving a request for a set of canonical digital images of anentity; generating logic executed by the processor for generating anumber of digital image search result sets, the search result setgeneration comprising querying a number of digital image data storesusing a number of queries, each query comprising a number of searchterms; selecting logic executed by the processor for selecting aplurality of candidate digital images from the number of digital imagesearch result sets, the plurality of candidate digital images beingselected using a relevancy score associated with each candidate digitalimage of the plurality; analyzing logic executed by the processor foranalyzing each candidate digital image to detect an object of a typecorresponding to an object type of the entity and to detect a number offiducial points of, the object type, in the detected object; determininglogic executed by the processor for determining an n-dimensional featurevector for a candidate digital image of the plurality using data ofpixels corresponding to the number of fiducial points of the objectdetected in the candidate digital image, the feature vectordetermination being performed for each candidate digital image of theplurality to determine a plurality of feature vectors; forming logicexecuted by the processor for forming a plurality of clusters using theplurality of feature vectors, each cluster of the plurality comprising anumber of feature vectors, each feature vector in each clustercorresponding to a candidate digital image of the plurality; andselecting logic executed by the processor for selecting a set ofcandidate digital images for the set of canonical digital images using anumber of clusters of the plurality, the candidate digital imageselection comprising determining, for each candidate digital image witha corresponding feature vector belonging to a cluster of the number ofclusters, a measure of quality based on at least one consideration ofquality, each candidate digital image of the set of candidate digitalimages having a higher measure of quality relative to the measure ofquality associated with each unselected candidate digital image.